Today more than ever, information plays an increasingly important role in the lives of people and companies. The Internet has transformed how goods and services are bought and sold between consumers, between businesses and consumers, and between businesses. In a macro sense, highly competitive business environments cannot afford to squander any resources. Better examination of the data stored on systems, and the value of the information can be crucial to better align company strategies with greater business goals. In a micro sense, decisions by machine processes can impact the way a system reacts and/or a human interacts to handling data.
A basic premise is that information affects performance. Accordingly, information has value because an entity (whether human or non-human) can typically take different actions depending on what is learned, thereby obtaining higher benefits or incurring lower costs as a result of knowing the information. In the context of a cost analysis, the value of information (VOI) can be calculated based on the value or cost obtained if action must be taken without information versus the value or cost obtained if information is first learned, and then action is taken. The difference between these two values or costs can then be called the economic VOI.
VOI can provide enormous benefits in many different areas. For example, VOI analysis has been applied to earth science data, horse racing, the stock market, and alert systems. In another example, accurate, timely, and relevant information saves transportation agencies both time and money through increased efficiency, improved productivity, and rapid deployment of innovations. In the realm of large government agencies, access to research results allows one agency to benefit from the experiences of other agencies and to avoid costly duplication of effort.
In more focused areas, where human interaction is an important factor, which is typically a factor in most, if not all, aspects of a business, businesses are continually seeking ways in which to maximize employee productivity. Interest has been growing in opportunities to build and deploy statistical models that can infer a computer user's current interruptability from computer activity and relevant contextual information. One system intermittently asks users to assess their perceived interruptability during a training phase and builds decision-theoretic models with the ability to predict the cost of interrupting the user. The system employs models at run-time to compute the expected cost of interruptions, and provides a mediator for incoming notifications, based on consideration of a user's current and recent history of computer activity, meeting status, location, time of day, and whether a conversation is detected, for example.
However, a human decision maker (e.g., the user) usually derives subjective probabilities about the quality of the information and will make use of additional information to “update” his or her prior beliefs. Where the decision maker is not human, but an algorithm, other factors can come into play. For example, algorithms that alert on constraint violations and threats in a straightforward manner can inundate a user in dynamic domains. In fields such as medical monitoring, unwanted alerts can be a problem in that alerts provided each second will quickly be processed differently (e.g., discarded) by the user in stressful situations, for example. Accordingly, to be useful, the algorithm needs to produce high-value, user-appropriate information.